def gen_basis_fns():
X, y = hepatitis()
gam = LinearGAM(lam=.6, fit_intercept=False).fit(X, y)
XX = gam.generate_X_grid()
plt.figure()
fig, ax = plt.subplots(2,1)
ax[0].plot(XX, gam._modelmat(XX, feature=0).A);
ax[0].set_title('b-Spline Basis Functions')
ax[1].scatter(X, y, facecolor='gray', edgecolors='none')
ax[1].plot(XX, gam._modelmat(XX).A * gam.coef_);
ax[1].plot(XX, gam.predict(XX), 'k')
ax[1].set_title('Fitted Model')
fig.tight_layout()
plt.savefig('imgs/pygam_basis.png', dpi=300)
def constraints():
X, y = hepatitis(return_X_y=True)
gam1 = LinearGAM(s(0, constraints='monotonic_inc')).fit(X, y)
gam2 = LinearGAM(s(0, constraints='concave')).fit(X, y)
fig, ax = plt.subplots(1, 2)
ax[0].plot(X, y, label='data')
ax[0].plot(X, gam1.predict(X), label='monotonic fit')
ax[0].legend()
ax[1].plot(X, y, label='data')
ax[1].plot(X, gam2.predict(X), label='concave fit')
ax[1].legend()
fig.tight_layout()
plt.savefig('imgs/pygam_constraints.png', dpi=300)
def constraints():
X, y = hepatitis()
plt.figure()
fig, ax = plt.subplots(1,2)
gam = LinearGAM(constraints='monotonic_inc').fit(X, y)
ax[0].plot(X, y, label='data')
ax[0].plot(X, gam.predict(X), label='monotonic fit')
ax[0].legend()
gam = LinearGAM(constraints='concave').fit(X, y)
ax[1].plot(X, y, label='data')
ax[1].plot(X, gam.predict(X), label='concave fit')
ax[1].legend()
fig.tight_layout()
plt.savefig('imgs/pygam_constraints.png', dpi=300)
def hepatitis_X_y():
# y is real
# recommend LinearGAM
return hepatitis(return_X_y=True)
XX = gam.generate_X_grid(term=i)
pdep, confi = gam.partial_dependence(term=i, width=.95)
ax.plot(XX[:, i], pdep)
ax.plot(XX[:, i], confi, c='r', ls='--')
ax.set_title(titles[i])
gam.accuracy(X, y)
######################################################
# constraints
from pygam import LinearGAM, s
from pygam.datasets import hepatitis
X, y = hepatitis(return_X_y=True)
X.shape
gam1 = LinearGAM(s(0, constraints='monotonic_inc')).fit(X, y)
gam2 = LinearGAM(s(0, constraints='concave')).fit(X, y)
fig, ax = plt.subplots(1, 2)
ax[0].plot(X, y, label='data')
ax[0].plot(X, gam1.predict(X), label='monotonic fit')
ax[0].legend()
ax[1].plot(X, y, label='data')
ax[1].plot(X, gam2.predict(X), label='concave fit')
ax[1].legend()
######################################################